SAVRN Model Hub
AI Training Datasets
Each dataset with its card, its structure (every split, row count and column type), its files, its license, and the models that disclose training on it.
11,122Models
3,164Datasets
285Papers
6,792Publishers
11,852Sourced relationships
Updated 2026-10-18 · How the library is built
3,164 datasets, sorted by most downloaded.
This dataset has a total of 364,420 pieces of medical QA data, some of which have multiple questions in different ways. We extract medical QA pairs from plain texts (e.g., medical encyclopedias and medical articles). We collected 8,699 encyclopedia entries for diseases and 2,736 encyclopedia entries for medicines on Chinese Wikipedia. Moreover, we crawled 226,432 high-quality medical articles from the Qianwen Health website. https://zh.wikipedia.org/wiki/ https://51zyzy.com/
A dataset of news about hurricanes and other tropical cyclones (typhoons, cyclones and tropical storms) from a global news index, January 2019 – July 2026, built with the Prism News API (historical and real-time news, https://radar-api.fliarbi.com/news). Every row is one news article about a tropical cyclone, with its date, location, tone, machine-assigned priority, themes and the organisations it names, plus a link that returns the full article through the API. - 49.84% of each year's tropical-cyclone coverage falls in August–October (2019–2025 average). - October 2024 (Hurricane Milton, after Hurricane Helene) was the peak month: 102.1 articles per 10,000 news articles. - The 2025 share…
日本語・今治弁のQAを用いて、reasoning effort に応じた思考文の生成を学習するための教師ありファインチューニング（SFT）用データセットです。Imabari Wiki QA v4 Validated の質問と回答を保持し、元記事の文脈を参照して思考文を再生成しています。 This dataset supports supervised fine-tuning (SFT) of reasoning-effort-conditioned explanations using Japanese QA with Imabari dialect expressions. Questions and answers from Imabari Wiki QA v4 Validated are preserved, while reasoning text is regenerated with context from the source articles. 本カードは LLM-jp 4 用のチャットテンプレートに合わせたデータ形式を説明します。思考文の生成モデルは、両形式とも Qwen3.8-27B-NVFP4 です。Qwen3.8版とLLM-jp 4版は、同じ質問・回答・生成済み思考文を共有し、メッセージのフィールド、effortラベル、トークン計数用トークナイザーが異なります。 This card describes the data format adapted to the LLM-jp 4 chat template. Both versions use…
日本語・今治弁のQAを用いて、reasoning effort に応じた思考文の生成を学習するための教師ありファインチューニング（SFT）用データセットです。Imabari Wiki QA v4 Validated の質問と回答を保持し、元記事の文脈を参照して思考文を再生成しています。 This dataset supports supervised fine-tuning (SFT) of reasoning-effort-conditioned explanations using Japanese QA with Imabari dialect expressions. Questions and answers from Imabari Wiki QA v4 Validated are preserved, while reasoning text is regenerated with context from the source articles. 本カードは Qwen3.8 用のチャットテンプレートに合わせたデータ形式を説明します。思考文の生成モデルは、両形式とも Qwen3.8-27B-NVFP4 です。Qwen3.8版とLLM-jp 4版は、同じ質問・回答・生成済み思考文を共有し、メッセージのフィールド、effortラベル、トークン計数用トークナイザーが異なります。 This card describes the data format adapted to the Qwen3.8 chat template. Both versions use…
Preparation notes and schema examples for Music tasks using Image Depth data. Full source material is intentionally not bundled, so provenance and licensing remain explicit. - preprocess.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows. The repository is suitable for testing the data…
This repository contains a preparation pipeline and a small metadata sample for NLP Summarization work with Image Depth inputs. It does not claim to be a complete benchmark release; the loader documents how source data is normalized and validated. - clean.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can…
Preparation notes and schema examples for Robotics tasks using Image Depth data. Full source material is intentionally not bundled, so provenance and licensing remain explicit. - preprocess.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows. The repository is suitable for testing the…
This data card accompanies a lightweight Education loader for Image Text metadata. It is meant for pipeline inspection, source adaptation, and reproducible split preparation. - prepare.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows. The repository is suitable for testing the data…
Paired RGB images, camera parameters, and complete camera-frustum point clouds. The point clouds contain all available source-mesh vertices inside each image's camera frustum, including vertices hidden behind visible surfaces. They are variable-length XYZ arrays in the OpenCV camera frame (x right, y down, z forward). No fixed point count, sampling, normalization, or patch assignment is applied to the stored targets. - assetsmanifest.json: one record per image, with relative paths, point count, coordinate frame, image derivation modes, and SHA256 hashes. - shards/imgfullgtXXXX.tar: independent archives containing complete sample files, grouped by scene. Each archive is at most 5,000,000,000…
Preparation notes and schema examples for Industrial tasks using Text Tabular data. Full source material is intentionally not bundled, so provenance and licensing remain explicit. - dataloader.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows. The repository is suitable for testing the…
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
Related SAVRN Research
The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.
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What open models cost to run
The same open-weight model priced by every host that serves it, per million tokens.
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Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.